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arXiv · 1605.08497

Universum Learning for SVM Regression

Sauptik Dhar·Vladimir Cherkassky
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Abstract

This paper extends the idea of Universum learning [18, 19] to regression problems. We propose new Universum-SVM formulation for regression problems that incorporates a priori knowledge in the form of additional data samples. These additional data samples or Universum belong to the same application domain as the training samples, but they follow a different distribution. Several empirical comparisons are presented to illustrate the utility of the proposed approach.

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Sauptik Dhar, Vladimir Cherkassky. 2016-05-27. Universum Learning for SVM Regression. https://arxiv.org/abs/1605.08497

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